def test_diagonal(self):
"""The whitened data should have all 1s on the covariance matrix."""
a = calculate_whitening_matrix(self.cnt)
dat2 = np.dot(self.cnt.data, a)
vals = np.diag(np.cov(dat2.T))
np.testing.assert_array_almost_equal(vals,
[1. for i in range(len(vals))])
def test_zeros(self):
"""The whinened data should have all 0s on the non-diagonals of the covariance matrix."""
a = calculate_whitening_matrix(self.cnt)
dat2 = np.dot(self.cnt.data, a)
cov = np.cov(dat2.T)
# substract the diagonals
cov -= np.diag(np.diag(cov))
self.assertAlmostEqual(np.sum(cov), 0)
def test_zeros(self):
"""The whitened data should have all 0s on the non-diagonals of the covariance matrix."""
a = calculate_whitening_matrix(self.cnt)
dat2 = apply_spatial_filter(self.cnt, a)
cov = np.cov(dat2.data.T)
# substract the diagonals
cov -= np.diag(np.diag(cov))
self.assertAlmostEqual(np.sum(cov), 0)
def test_calculate_whitening_matrix_copy(self):
"""calculate_whitening_matrix must not modify arguments."""
cpy = self.cnt.copy()
calculate_whitening_matrix(self.cnt)
self.assertEqual(self.cnt, cpy)
def test_shape(self):
"""The whitening filter should have the shape: CHANSxCHANS."""
a = calculate_whitening_matrix(self.cnt)
self.assertEqual(a.shape, (CHANS, CHANS))
def test_calculate_whitening_matrix_copy(self):
"""calculate_whitening_matrix must not modify arguments."""
cpy = self.cnt.copy()
calculate_whitening_matrix(self.cnt)
self.assertEqual(self.cnt, cpy)
def test_diagonal(self):
"""The whitened data should have all 1s on the covariance matrix."""
a = calculate_whitening_matrix(self.cnt)
dat2 = apply_spatial_filter(self.cnt, a)
vals = np.diag(np.cov(dat2.data.T))
np.testing.assert_array_almost_equal(vals, [1. for i in range(len(vals))])
def test_shape(self):
"""The whitening filter should have the shape: CHANSxCHANS."""
a = calculate_whitening_matrix(self.cnt)
self.assertEqual(a.shape, (CHANS, CHANS))
